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Deep Learning with R
Getting Started With The Outline
Introduction and Outline (7:22)
Fundamental Concepts in Deep Learning (4:33)
Classification with Two-Layers Artificial Neural Networks (7:29)
Probabilistic Predictions with Two-Layer ANNs (8:24)
Tuning ANNs Hyper-Parameters and Best Practices (5:56)
Neural Network Architectures (5:59)
Neural Network Architectures (Conti (3:28)
Optimization Algorithms and Stochastic Gradient Descent (7:52)
Backpropagation (1:33)
Backpropagation (4:45)
Introduction to Convolutional Neural Networks (11:57)
CNNs in R (4:28)
Classifying Real-World Images with Pre-Trained Models (4:05)
Introduction to Long Short-Term Memory (4:22)
RNNs in R (14:49)
Use-Case – Learning How to Spell English Words from Scratch (3:42)
Autoencoders (1:40)
Restricted Boltzmann Machines and Deep Belief Networks (3:00)
Reinforcement Learning with ANNs (9:02)
Use-Case – Anomaly Detection through Denoising Autoencoders (7:51)
Deep Learning for Natural Language Processings (19:09)
Deep Learning for Complex Multimodal Tasks (23:20)
Other Important Applications of Deep Learning (3:51)
A Complete Comparison of Every DL Packages in R (5:43)
Research Directions and Open Questions (30:04)
CNNs in R
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